The present invention relates to blind separation of signals. More specifically, this invention relates to blind separation of signals also called direction of arrival of signals generated by sources that are correlated.
Power generation main components (e.g., turbines, generators, boilers, transformers as well as related auxiliary equipment such as boiler feed pumps, cooling water pumps, fans, valves, exhaust gas cleaning systems) operational issues are normally characterized by changes in their acoustic signatures (in sonic and most importantly, ultrasonic ranges), e.g., arcing, bearing or lubrication issues, loose parts, leaks, etc. Contact based acoustic emission monitoring of all subcomponents of e.g., auxiliary equipment that is non-critical for the continuation of the plant operation is generally uneconomic. Non-contact, microphone-based systems could monitor a large area with a multitude of different types of equipments simultaneously. The concept of this area monitoring is illustrated in FIG. 1. Here, a fault is identified to come from the device under test (DUT) number 2 using a microphone array containing sensors or microphones numbers 1, 2 and 3 and source localization instructions implemented on a processor.
Challenges of this scenario are as follows. State-of-the art source separation and localization approaches that use time-frequency masking will have problems due to the broad band characteristics of acoustic emissions from power generation equipment and the ensuing signal overlap in the time-frequency domain. In particular, the commonly used sparseness assumption that signals be disjoint in the time-frequency domain is violated and algorithms that build on this assumption, like e.g. DUET, will fail. Another assumption required by many algorithms like ESPRIT, MUSIC or Weighted Subspace Fitting such as disclosed in “M. Jansson, A. L. Swindlehurst, and B. Ottersten. Weighted subspace fitting for general array error models. IEEE Transactions on Signal Processing, 46: 2484-2498, 1997” and “T. Melia and S. Rickard. Underdetermined blind source separation in echoic environments using desprit, 2007” is that all signals be independent. This assumption may be approximately true for speech signals but not necessarily for machine vibrations.
Accordingly, improved and novel methods and systems for blind separation of signals that are not disjoint and/or independent and/or uncorrelated are required.